Computerized systems for generating data segments for use in decision systems are known. These systems, however, lack the ability to generate data segments in real time that reflect accurate information about a user's condition, including a combination of user-disclosed data, user-inferred data, and user situation/activity data. Prior systems use techniques such as internet-based cookie-level user attributes, manual content-based targeting, and manual event targeting to make decisions that may be used to generate recommendations, target messages, or take other actions customized to users
Audience targeting using cookie-level audience attributes from first party or third party sources has been used to target messages to an individual online. Content targeting, such as directing messages to consumers of certain types of content (e.g., football games) has been done manually by choosing to schedule advertisements or display other messages during such programming. Manual event targeting has been performed by predetermining a one-off event like a win of a given game and manually allocating creative options based on the outcome once the outcome is known.
Targeting in a television environment is generally limited to digital solutions and non-1:1 environments. Current systems are not capable of such automated, real-time targeting or customization at scale, requiring manual changes at a production switchboard.
Prior art systems are not designed to address the specific user conditions such as an inferred emotional state of a user, enabling 1:1 targeting to that user; they are only capable of mass targeting using rules-based decisions determined across sets of qualifying users.